Navigation has been classically solved in robotics through the combination of
SLAM and planning. More recently, beyond waypoint planning, problems involving
significant components of (visual) high-level reasoning have been explored in
simulated environments, mostly addressed with large-scale machine learning, in
particular RL, offline-RL or imitation learning. These methods require the
agent to learn various skills like local planning, mapping objects and querying
the learned spatial representations. In contrast to simpler tasks like waypoint
planning (PointGoal), for these more complex tasks the current state-of-the-art
models have been thoroughly evaluated in simulation but, to our best knowledge,
not yet in real environments.
In this work we focus on sim2real transfer. We target the challenging
Multi-Object Navigation (Multi-ON) task and port it to a physical environment
containing real replicas of the originally virtual Multi-ON objects. We
introduce a hybrid navigation method, which decomposes the problem into two
different skills: (1) waypoint navigation is addressed with classical SLAM
combined with a symbolic planner, whereas (2) exploration, semantic mapping and
goal retrieval are dealt with deep neural networks trained with a combination
of supervised learning and RL. We show the advantages of this approach compared
to end-to-end methods both in simulation and a real environment and outperform
the SOTA for this task.

我们提出了一种混合导航方法，将多对象导航（Multi-ON）任务分解为两个不同的技能：（1）使用经典 SLAM 和符号规划器处理航路点导航，而（2）使用结合监督学习和强化学习训练的深度神经网络处理探索、语义建图和目标检索，我们展示了该方法在模拟和真实环境中相对于端到端方法的优势，并超越了该任务的最先进技术。

在真实环境中使用混合策略进行多目标导航

Multi-Object Navigation in real environments using hybrid policies

The Multi-Object Navigation (MultiON) task requires a robot to localize an
instance (each) of multiple object classes. It is a fundamental task for an
assistive robot in a home or a factory. Existing methods for MultiON have
viewed this as a direct extension of Object Navigation (ON), the task of
localising an instance of one object class, and are pre-sequenced, i.e., the
sequence in which the object classes are to be explored is provided in advance.
This is a strong limitation in practical applications characterized by dynamic
changes. This paper describes a deep reinforcement learning framework for
sequence-agnostic MultiON based on an actor-critic architecture and a suitable
reward specification. Our framework leverages past experiences and seeks to
reward progress toward individual as well as multiple target object classes. We
use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat
3D simulation environment to experimentally show that our method performs
better than a pre-sequenced approach and a state of the art ON method extended
to MultiON.

利用深度强化学习模型，基于无序的 actor-critic 框架和适当的奖励规范，提出了一个适用于多目标导航的机器人的算法，通过实验验证了这个方法在实践应用中胜过了预设顺序的方法和当下最先进的单个目标导航的方法。

序列无关的多物体导航

Sequence-Agnostic Multi-Object Navigation

Our work focuses on the Multi-Object Navigation (MultiON) task, where an
agent needs to navigate to multiple objects in a given sequence. We
systematically investigate the inherent modularity of this task by dividing our
approach to contain four modules: (a) an object detection module trained to
identify objects from RGB images, (b) a map building module to build a semantic
map of the observed objects, (c) an exploration module enabling the agent to
explore its surroundings, and finally (d) a navigation module to move to
identified target objects. We focus on the navigation and the exploration
modules in this work. We show that we can effectively leverage a PointGoal
navigation model in the MultiON task instead of learning to navigate from
scratch. Our experiments show that a PointGoal agent-based navigation module
outperforms analytical path planning on the MultiON task. We also compare
exploration strategies and surprisingly find that a random exploration strategy
significantly outperforms more advanced exploration methods. We additionally
create MultiON 2.0, a new large-scale dataset as a test-bed for our approach.

本文研究了多目标导航任务，通过四个模块（物体检测、语义地图构建、环境探索和导航）来解决此任务。结果表明，在 MultiON 任务中，采用 PointGoal 导航模型优于从头开始学习导航。与分析路径规划相比，基于 Agent 的导航模块表现更好。同时也探讨了探索策略并发现随机探索策略显著优于更高级的探索方法。我们还创建了 MultiON 2.0 数据集作为我们方法的测试平台。